Addestramento di un modello AutoML Edge mediante l'API Vertex AI

 import com.google.cloud.aiplatform.v1.DeployedModelRef; import com.google.cloud.aiplatform.v1.EnvVar; import com.google.cloud.aiplatform.v1.FilterSplit; import com.google.cloud.aiplatform.v1.FractionSplit; import com.google.cloud.aiplatform.v1.InputDataConfig; import com.google.cloud.aiplatform.v1.Model; import com.google.cloud.aiplatform.v1.ModelContainerSpec; import com.google.cloud.aiplatform.v1.PipelineServiceClient; import com.google.cloud.aiplatform.v1.PipelineServiceSettings; import com.google.cloud.aiplatform.v1.Port; import com.google.cloud.aiplatform.v1.PredefinedSplit; import com.google.cloud.aiplatform.v1.PredictSchemata; import com.google.cloud.aiplatform.v1.TimestampSplit; import com.google.cloud.aiplatform.v1.TrainingPipeline; import com.google.cloud.aiplatform.v1.TrainingPipelineName; import com.google.rpc.Status; import java.io.IOException;  public class GetTrainingPipelineSample {   public static void main(String[] args) throws IOException {     // TODO(developer): Replace these variables before running the sample.     String project = "YOUR_PROJECT_ID";     String trainingPipelineId = "YOUR_TRAINING_PIPELINE_ID";     getTrainingPipeline(project, trainingPipelineId);   }    static void getTrainingPipeline(String project, String trainingPipelineId) throws IOException {     PipelineServiceSettings pipelineServiceSettings =         PipelineServiceSettings.newBuilder()             .setEndpoint("us-central1-aiplatform.googleapis.com:443")             .build();      // Initialize client that will be used to send requests. This client only needs to be created     // once, and can be reused for multiple requests. After completing all of your requests, call     // the "close" method on the client to safely clean up any remaining background resources.     try (PipelineServiceClient pipelineServiceClient =         PipelineServiceClient.create(pipelineServiceSettings)) {       String location = "us-central1";       TrainingPipelineName trainingPipelineName =           TrainingPipelineName.of(project, location, trainingPipelineId);        TrainingPipeline trainingPipelineResponse =           pipelineServiceClient.getTrainingPipeline(trainingPipelineName);        System.out.println("Get Training Pipeline Response");       System.out.format("\tName: %s\n", trainingPipelineResponse.getName());       System.out.format("\tDisplay Name: %s\n", trainingPipelineResponse.getDisplayName());       System.out.format(           "\tTraining Task Definition: %s\n", trainingPipelineResponse.getTrainingTaskDefinition());       System.out.format(           "\tTraining Task Inputs: %s\n", trainingPipelineResponse.getTrainingTaskInputs());       System.out.format(           "\tTraining Task Metadata: %s\n", trainingPipelineResponse.getTrainingTaskMetadata());       System.out.format("\tState: %s\n", trainingPipelineResponse.getState());       System.out.format("\tCreate Time: %s\n", trainingPipelineResponse.getCreateTime());       System.out.format("\tStart Time: %s\n", trainingPipelineResponse.getStartTime());       System.out.format("\tEnd Time: %s\n", trainingPipelineResponse.getEndTime());       System.out.format("\tUpdate Time: %s\n", trainingPipelineResponse.getUpdateTime());       System.out.format("\tLabels: %s\n", trainingPipelineResponse.getLabelsMap());       InputDataConfig inputDataConfig = trainingPipelineResponse.getInputDataConfig();        System.out.println("\tInput Data Config");       System.out.format("\t\tDataset Id: %s\n", inputDataConfig.getDatasetId());       System.out.format("\t\tAnnotations Filter: %s\n", inputDataConfig.getAnnotationsFilter());       FractionSplit fractionSplit = inputDataConfig.getFractionSplit();        System.out.println("\t\tFraction Split");       System.out.format("\t\t\tTraining Fraction: %s\n", fractionSplit.getTrainingFraction());       System.out.format("\t\t\tValidation Fraction: %s\n", fractionSplit.getValidationFraction());       System.out.format("\t\t\tTest Fraction: %s\n", fractionSplit.getTestFraction());       FilterSplit filterSplit = inputDataConfig.getFilterSplit();        System.out.println("\t\tFilter Split");       System.out.format("\t\t\tTraining Filter: %s\n", filterSplit.getTrainingFilter());       System.out.format("\t\t\tValidation Filter: %s\n", filterSplit.getValidationFilter());       System.out.format("\t\t\tTest Filter: %s\n", filterSplit.getTestFilter());       PredefinedSplit predefinedSplit = inputDataConfig.getPredefinedSplit();        System.out.println("\t\tPredefined Split");       System.out.format("\t\t\tKey: %s\n", predefinedSplit.getKey());       TimestampSplit timestampSplit = inputDataConfig.getTimestampSplit();        System.out.println("\t\tTimestamp Split");       System.out.format("\t\t\tTraining Fraction: %s\n", timestampSplit.getTrainingFraction());       System.out.format("\t\t\tTest Fraction: %s\n", timestampSplit.getTestFraction());       System.out.format("\t\t\tValidation Fraction: %s\n", timestampSplit.getValidationFraction());       System.out.format("\t\t\tKey: %s\n", timestampSplit.getKey());       Model modelResponse = trainingPipelineResponse.getModelToUpload();        System.out.println("\t\tModel to upload");       System.out.format("\t\tName: %s\n", modelResponse.getName());       System.out.format("\t\tDisplay Name: %s\n", modelResponse.getDisplayName());       System.out.format("\t\tDescription: %s\n", modelResponse.getDescription());       System.out.format("\t\tMetadata Schema Uri: %s\n", modelResponse.getMetadataSchemaUri());       System.out.format("\t\tMeta Data: %s\n", modelResponse.getMetadata());       System.out.format("\t\tTraining Pipeline: %s\n", modelResponse.getTrainingPipeline());       System.out.format("\t\tArtifact Uri: %s\n", modelResponse.getArtifactUri());       System.out.format(           "\t\tSupported Deployment Resources Types: %s\n",           modelResponse.getSupportedDeploymentResourcesTypesList().toString());       System.out.format(           "\t\tSupported Input Storage Formats: %s\n",           modelResponse.getSupportedInputStorageFormatsList().toString());       System.out.format(           "\t\tSupported Output Storage Formats: %s\n",           modelResponse.getSupportedOutputStorageFormatsList().toString());       System.out.format("\t\tCreate Time: %s\n", modelResponse.getCreateTime());       System.out.format("\t\tUpdate Time: %s\n", modelResponse.getUpdateTime());       System.out.format("\t\tLabels: %s\n", modelResponse.getLabelsMap());       PredictSchemata predictSchemata = modelResponse.getPredictSchemata();        System.out.println("\tPredict Schemata");       System.out.format("\t\tInstance Schema Uri: %s\n", predictSchemata.getInstanceSchemaUri());       System.out.format(           "\t\tParameters Schema Uri: %s\n", predictSchemata.getParametersSchemaUri());       System.out.format(           "\t\tPrediction Schema Uri: %s\n", predictSchemata.getPredictionSchemaUri());        for (Model.ExportFormat supportedExportFormat :           modelResponse.getSupportedExportFormatsList()) {         System.out.println("\tSupported Export Format");         System.out.format("\t\tId: %s\n", supportedExportFormat.getId());       }       ModelContainerSpec containerSpec = modelResponse.getContainerSpec();        System.out.println("\tContainer Spec");       System.out.format("\t\tImage Uri: %s\n", containerSpec.getImageUri());       System.out.format("\t\tCommand: %s\n", containerSpec.getCommandList());       System.out.format("\t\tArgs: %s\n", containerSpec.getArgsList());       System.out.format("\t\tPredict Route: %s\n", containerSpec.getPredictRoute());       System.out.format("\t\tHealth Route: %s\n", containerSpec.getHealthRoute());        for (EnvVar envVar : containerSpec.getEnvList()) {         System.out.println("\t\tEnv");         System.out.format("\t\t\tName: %s\n", envVar.getName());         System.out.format("\t\t\tValue: %s\n", envVar.getValue());       }        for (Port port : containerSpec.getPortsList()) {         System.out.println("\t\tPort");         System.out.format("\t\t\tContainer Port: %s\n", port.getContainerPort());       }        for (DeployedModelRef deployedModelRef : modelResponse.getDeployedModelsList()) {         System.out.println("\tDeployed Model");         System.out.format("\t\tEndpoint: %s\n", deployedModelRef.getEndpoint());         System.out.format("\t\tDeployed Model Id: %s\n", deployedModelRef.getDeployedModelId());       }        Status status = trainingPipelineResponse.getError();       System.out.println("\tError");       System.out.format("\t\tCode: %s\n", status.getCode());       System.out.format("\t\tMessage: %s\n", status.getMessage());     }   } }

from google.cloud import aiplatform   def get_training_pipeline_sample(     project: str,     training_pipeline_id: str,     location: str = "us-central1",     api_endpoint: str = "us-central1-aiplatform.googleapis.com", ):     # The AI Platform services require regional API endpoints.     client_options = {"api_endpoint": api_endpoint}     # Initialize client that will be used to create and send requests.     # This client only needs to be created once, and can be reused for multiple requests.     client = aiplatform.gapic.PipelineServiceClient(client_options=client_options)     name = client.training_pipeline_path(         project=project, location=location, training_pipeline=training_pipeline_id     )     response = client.get_training_pipeline(name=name)     print("response:", response)  

 import com.google.cloud.aiplatform.v1.DeployedModelRef; import com.google.cloud.aiplatform.v1.EnvVar; import com.google.cloud.aiplatform.v1.Model; import com.google.cloud.aiplatform.v1.Model.ExportFormat; import com.google.cloud.aiplatform.v1.ModelContainerSpec; import com.google.cloud.aiplatform.v1.ModelName; import com.google.cloud.aiplatform.v1.ModelServiceClient; import com.google.cloud.aiplatform.v1.ModelServiceSettings; import com.google.cloud.aiplatform.v1.Port; import com.google.cloud.aiplatform.v1.PredictSchemata; import java.io.IOException;  public class GetModelSample {    public static void main(String[] args) throws IOException {     // TODO(developer): Replace these variables before running the sample.     String project = "YOUR_PROJECT_ID";     String modelId = "YOUR_MODEL_ID";     getModelSample(project, modelId);   }    static void getModelSample(String project, String modelId) throws IOException {     ModelServiceSettings modelServiceSettings =         ModelServiceSettings.newBuilder()             .setEndpoint("us-central1-aiplatform.googleapis.com:443")             .build();      // Initialize client that will be used to send requests. This client only needs to be created     // once, and can be reused for multiple requests. After completing all of your requests, call     // the "close" method on the client to safely clean up any remaining background resources.     try (ModelServiceClient modelServiceClient = ModelServiceClient.create(modelServiceSettings)) {       String location = "us-central1";       ModelName modelName = ModelName.of(project, location, modelId);        Model modelResponse = modelServiceClient.getModel(modelName);       System.out.println("Get Model response");       System.out.format("\tName: %s\n", modelResponse.getName());       System.out.format("\tDisplay Name: %s\n", modelResponse.getDisplayName());       System.out.format("\tDescription: %s\n", modelResponse.getDescription());        System.out.format("\tMetadata Schema Uri: %s\n", modelResponse.getMetadataSchemaUri());       System.out.format("\tMetadata: %s\n", modelResponse.getMetadata());       System.out.format("\tTraining Pipeline: %s\n", modelResponse.getTrainingPipeline());       System.out.format("\tArtifact Uri: %s\n", modelResponse.getArtifactUri());        System.out.format(           "\tSupported Deployment Resources Types: %s\n",           modelResponse.getSupportedDeploymentResourcesTypesList());       System.out.format(           "\tSupported Input Storage Formats: %s\n",           modelResponse.getSupportedInputStorageFormatsList());       System.out.format(           "\tSupported Output Storage Formats: %s\n",           modelResponse.getSupportedOutputStorageFormatsList());        System.out.format("\tCreate Time: %s\n", modelResponse.getCreateTime());       System.out.format("\tUpdate Time: %s\n", modelResponse.getUpdateTime());       System.out.format("\tLabels: %s\n", modelResponse.getLabelsMap());        PredictSchemata predictSchemata = modelResponse.getPredictSchemata();       System.out.println("\tPredict Schemata");       System.out.format("\t\tInstance Schema Uri: %s\n", predictSchemata.getInstanceSchemaUri());       System.out.format(           "\t\tParameters Schema Uri: %s\n", predictSchemata.getParametersSchemaUri());       System.out.format(           "\t\tPrediction Schema Uri: %s\n", predictSchemata.getPredictionSchemaUri());        for (ExportFormat exportFormat : modelResponse.getSupportedExportFormatsList()) {         System.out.println("\tSupported Export Format");         System.out.format("\t\tId: %s\n", exportFormat.getId());       }        ModelContainerSpec containerSpec = modelResponse.getContainerSpec();       System.out.println("\tContainer Spec");       System.out.format("\t\tImage Uri: %s\n", containerSpec.getImageUri());       System.out.format("\t\tCommand: %s\n", containerSpec.getCommandList());       System.out.format("\t\tArgs: %s\n", containerSpec.getArgsList());       System.out.format("\t\tPredict Route: %s\n", containerSpec.getPredictRoute());       System.out.format("\t\tHealth Route: %s\n", containerSpec.getHealthRoute());        for (EnvVar envVar : containerSpec.getEnvList()) {         System.out.println("\t\tEnv");         System.out.format("\t\t\tName: %s\n", envVar.getName());         System.out.format("\t\t\tValue: %s\n", envVar.getValue());       }        for (Port port : containerSpec.getPortsList()) {         System.out.println("\t\tPort");         System.out.format("\t\t\tContainer Port: %s\n", port.getContainerPort());       }        for (DeployedModelRef deployedModelRef : modelResponse.getDeployedModelsList()) {         System.out.println("\tDeployed Model");         System.out.format("\t\tEndpoint: %s\n", deployedModelRef.getEndpoint());         System.out.format("\t\tDeployed Model Id: %s\n", deployedModelRef.getDeployedModelId());       }     }   } }

/**  * TODO(developer): Uncomment these variables before running the sample.\  * (Not necessary if passing values as arguments)  */  // const modelId = 'YOUR_MODEL_ID'; // const project = 'YOUR_PROJECT_ID'; // const location = 'YOUR_PROJECT_LOCATION';  // Imports the Google Cloud Model Service Client library const {ModelServiceClient} = require('@google-cloud/aiplatform');  // Specifies the location of the api endpoint const clientOptions = {   apiEndpoint: 'us-central1-aiplatform.googleapis.com', };  // Instantiates a client const modelServiceClient = new ModelServiceClient(clientOptions);  async function getModel() {   // Configure the parent resource   const name = `projects/${project}/locations/${location}/models/${modelId}`;   const request = {     name,   };   // Get and print out a list of all the endpoints for this resource   const [response] = await modelServiceClient.getModel(request);    console.log('Get model response');   console.log(`\tName : ${response.name}`);   console.log(`\tDisplayName : ${response.displayName}`);   console.log(`\tDescription : ${response.description}`);   console.log(`\tMetadata schema uri : ${response.metadataSchemaUri}`);   console.log(`\tMetadata : ${JSON.stringify(response.metadata)}`);   console.log(`\tTraining pipeline : ${response.trainingPipeline}`);   console.log(`\tArtifact uri : ${response.artifactUri}`);   console.log(     `\tSupported deployment resource types : \       ${response.supportedDeploymentResourceTypes}`   );   console.log(     `\tSupported input storage formats : \       ${response.supportedInputStorageFormats}`   );   console.log(     `\tSupported output storage formats : \       ${response.supportedOutputStoragFormats}`   );   console.log(`\tCreate time : ${JSON.stringify(response.createTime)}`);   console.log(`\tUpdate time : ${JSON.stringify(response.updateTime)}`);   console.log(`\tLabels : ${JSON.stringify(response.labels)}`);    const predictSchemata = response.predictSchemata;   console.log('\tPredict schemata');   console.log(`\tInstance schema uri : ${predictSchemata.instanceSchemaUri}`);   console.log(     `\tParameters schema uri : ${predictSchemata.prametersSchemaUri}`   );   console.log(     `\tPrediction schema uri : ${predictSchemata.predictionSchemaUri}`   );    const [supportedExportFormats] = response.supportedExportFormats;   console.log('\tSupported export formats');   console.log(`\t${supportedExportFormats}`);    const containerSpec = response.containerSpec;   console.log('\tContainer Spec');   if (!containerSpec) {     console.log(`\t\t${JSON.stringify(containerSpec)}`);     console.log('\t\tImage uri : {}');     console.log('\t\tCommand : {}');     console.log('\t\tArgs : {}');     console.log('\t\tPredict route : {}');     console.log('\t\tHealth route : {}');     console.log('\t\tEnv');     console.log('\t\t\t{}');     console.log('\t\tPort');     console.log('\t\t{}');   } else {     console.log(`\t\t${JSON.stringify(containerSpec)}`);     console.log(`\t\tImage uri : ${containerSpec.imageUri}`);     console.log(`\t\tCommand : ${containerSpec.command}`);     console.log(`\t\tArgs : ${containerSpec.args}`);     console.log(`\t\tPredict route : ${containerSpec.predictRoute}`);     console.log(`\t\tHealth route : ${containerSpec.healthRoute}`);     const env = containerSpec.env;     console.log('\t\tEnv');     console.log(`\t\t\t${JSON.stringify(env)}`);     const ports = containerSpec.ports;     console.log('\t\tPort');     console.log(`\t\t\t${JSON.stringify(ports)}`);   }    const [deployedModels] = response.deployedModels;   console.log('\tDeployed models');   console.log('\t\t', deployedModels); } getModel();

def get_model_sample(project: str, location: str, model_name: str):      aiplatform.init(project=project, location=location)      model = aiplatform.Model(model_name=model_name)      print(model.display_name)     print(model.resource_name)     return model